Machine-learning-aided prediction of flexural strength and asr expansion for waste glass cementitious composite

Machine-learning-aided prediction of flexural strength and asr expansion for waste glass cementitious composite

Waste glass (WG) is unsustainable due to its nonbiodegradable property. However, its main ingredient is silicon dioxide, which can be utilised as a supplementary cementitious material. Before reusing WG, the flexural strength (FS) and alkali–silica reaction (ASR) expansion of WG concrete are two ess...

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Bibliographic Details
Main Authors: Sun, Junbo, Wang, Y., Yao, X., Ren, Z., Zhang, G., Zhang, C., Chen, X., Ma, W., Wang, Xiangyu
Format: Journal Article
Language:English
Published: MDPI 2021
Subjects:
Science & Technology
Physical Sciences
Technology
Chemistry, Multidisciplinary
Engineering, Multidisciplinary
Materials Science, Multidisciplinary
Physics, Applied
Chemistry
Engineering
Materials Science
Physics
random forest
beetle antennae search
activation methodology
machine learning
flexural strength
COMPRESSIVE STRENGTH
CONCRETE
KNOWLEDGE
FRAMEWORK
BEHAVIOR
SEARCH
SILICA
SAND
Online Access:http://purl.org/au-research/grants/arc/LP180100222
http://hdl.handle.net/20.500.11937/90922

Internet

http://purl.org/au-research/grants/arc/LP180100222
http://hdl.handle.net/20.500.11937/90922

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